Overview

Dataset statistics

Number of variables28
Number of observations37521
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 MiB
Average record size in memory224.0 B

Variable types

Numeric23
Categorical4
Boolean1

Alerts

name has a high cardinality: 36417 distinct values High cardinality
host_name has a high cardinality: 6878 distinct values High cardinality
price is highly correlated with accommodatesHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
availability_365 is highly correlated with availability_60 and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
accommodates is highly correlated with price and 2 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_60 is highly correlated with availability_365 and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
availability_90 is highly correlated with availability_365 and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_cleanliness and 3 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_checkin is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_communication is highly correlated with review_scores_accuracy and 2 other fieldsHigh correlation
review_scores_value is highly correlated with review_scores_accuracy and 2 other fieldsHigh correlation
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
price is highly correlated with accommodatesHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
availability_365 is highly correlated with availability_60 and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
accommodates is highly correlated with price and 2 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_60 is highly correlated with availability_365 and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
availability_90 is highly correlated with availability_365 and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_cleanliness and 4 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_accuracy and 3 other fieldsHigh correlation
review_scores_checkin is highly correlated with review_scores_accuracy and 3 other fieldsHigh correlation
review_scores_communication is highly correlated with review_scores_accuracy and 3 other fieldsHigh correlation
review_scores_location is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_value is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
availability_365 is highly correlated with availability_60 and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
accommodates is highly correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_60 is highly correlated with availability_365 and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with number_of_reviews_ltmHigh correlation
availability_90 is highly correlated with availability_365 and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_valueHigh correlation
review_scores_checkin is highly correlated with review_scores_communicationHigh correlation
review_scores_communication is highly correlated with review_scores_checkinHigh correlation
review_scores_value is highly correlated with review_scores_accuracyHigh correlation
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
neighbourhood is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with neighbourhoodHigh correlation
longitude is highly correlated with neighbourhoodHigh correlation
price is highly correlated with accommodatesHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
availability_365 is highly correlated with availability_60 and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
accommodates is highly correlated with price and 2 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_60 is highly correlated with availability_365 and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
availability_90 is highly correlated with availability_365 and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_cleanliness and 4 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
review_scores_checkin is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
review_scores_communication is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
review_scores_location is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
review_scores_value is highly correlated with review_scores_accuracy and 4 other fieldsHigh correlation
number_of_reviews_ltm is highly skewed (γ1 = 47.0573508) Skewed
bedrooms is highly skewed (γ1 = 20.52309613) Skewed
beds is highly skewed (γ1 = 28.73226247) Skewed
name is uniformly distributed Uniform
id has unique values Unique
availability_365 has 17285 (46.1%) zeros Zeros
number_of_reviews_ltm has 19197 (51.2%) zeros Zeros
bedrooms has 7108 (18.9%) zeros Zeros
availability_60 has 19762 (52.7%) zeros Zeros
number_of_reviews_l30d has 28166 (75.1%) zeros Zeros
availability_90 has 18977 (50.6%) zeros Zeros

Reproduction

Analysis started2022-03-01 16:53:24.142709
Analysis finished2022-03-01 16:55:35.316415
Duration2 minutes and 11.17 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct37521
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25630251.14
Minimum5396
Maximum53669791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:36.060843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5396
5-th percentile1997684
Q112153866
median24773718
Q338903973
95-th percentile51260686
Maximum53669791
Range53664395
Interquartile range (IQR)26750107

Descriptive statistics

Standard deviation15758082.89
Coefficient of variation (CV)0.6148235851
Kurtosis-1.187175732
Mean25630251.14
Median Absolute Deviation (MAD)13360405
Skewness0.1010031593
Sum9.61672653 × 1011
Variance2.483171764 × 1014
MonotonicityStrictly increasing
2022-03-01T17:55:36.343309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53961
 
< 0.1%
346627321
 
< 0.1%
346437631
 
< 0.1%
346438761
 
< 0.1%
346443641
 
< 0.1%
346447411
 
< 0.1%
346450171
 
< 0.1%
346456891
 
< 0.1%
346467781
 
< 0.1%
346471231
 
< 0.1%
Other values (37511)37511
> 99.9%
ValueCountFrequency (%)
53961
< 0.1%
73971
< 0.1%
79641
< 0.1%
99521
< 0.1%
105861
< 0.1%
105881
< 0.1%
109171
< 0.1%
112131
< 0.1%
112651
< 0.1%
114871
< 0.1%
ValueCountFrequency (%)
536697911
< 0.1%
536579651
< 0.1%
536406871
< 0.1%
536382441
< 0.1%
536173911
< 0.1%
536166681
< 0.1%
536002061
< 0.1%
535989411
< 0.1%
535904611
< 0.1%
535903361
< 0.1%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct36417
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size293.3 KiB
RARE - Gorgeous Apartment in the Haut-Marais!
 
17
Gorgeous studio in the Haut-Marais
 
14
Studio
 
12
 
12
Studio in the heart of Paris
 
11
Other values (36412)
37455 

Length

Max length255
Median length38
Mean length37.83332001
Min length1

Characters and Unicode

Total characters81
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35740 ?
Unique (%)95.3%

Sample

1st rowExplore the heart of old Paris
2nd rowMARAIS - 2ROOMS APT - 2/4 PEOPLE
3rd rowLarge & sunny flat with balcony !
4th rowParis petit coin douillet
5th rowStudio 7 Montmartre

Common Values

ValueCountFrequency (%)
RARE - Gorgeous Apartment in the Haut-Marais!17
 
< 0.1%
Gorgeous studio in the Haut-Marais14
 
< 0.1%
Studio12
 
< 0.1%
12
 
< 0.1%
Studio in the heart of Paris11
 
< 0.1%
RARE - Gorgeous Studio close from NATION!10
 
< 0.1%
Charmant studio au coeur de Paris10
 
< 0.1%
Gorgeous Apartment in the Haut-Marais!10
 
< 0.1%
Charmant appartement parisien9
 
< 0.1%
Charming flat in the heart of Paris8
 
< 0.1%
Other values (36407)37408
99.7%

Length

2022-03-01T17:55:36.719983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9940
 
4.2%
paris9638
 
4.1%
in6900
 
2.9%
studio6615
 
2.8%
appartement5254
 
2.2%
de4804
 
2.0%
apartment4456
 
1.9%
the4104
 
1.7%
cosy3895
 
1.7%
flat3765
 
1.6%
Other values (10117)175502
74.7%

Most occurring characters

ValueCountFrequency (%)
81
100.0%

Most occurring categories

ValueCountFrequency (%)
Control81
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common81
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
81
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII81
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
100.0%

host_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct30309
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93512321.78
Minimum2626
Maximum434633507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:36.959876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2626
5-th percentile2381076
Q112466456
median37335976
Q3135922211
95-th percentile367949245
Maximum434633507
Range434630881
Interquartile range (IQR)123455755

Descriptive statistics

Standard deviation115953053.4
Coefficient of variation (CV)1.239976199
Kurtosis0.8936561972
Mean93512321.78
Median Absolute Deviation (MAD)31184561
Skewness1.437951362
Sum3.508675826 × 1012
Variance1.344511059 × 1016
MonotonicityNot monotonic
2022-03-01T17:55:37.473925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
402191311205
 
0.5%
17037121157
 
0.4%
7642792143
 
0.4%
50978178133
 
0.4%
291007369114
 
0.3%
37358256194
 
0.3%
266737089
 
0.2%
2698105480
 
0.2%
210747877
 
0.2%
14701868575
 
0.2%
Other values (30299)36354
96.9%
ValueCountFrequency (%)
26262
< 0.1%
67924
< 0.1%
79031
 
< 0.1%
94121
 
< 0.1%
151461
 
< 0.1%
188761
 
< 0.1%
191051
 
< 0.1%
206331
 
< 0.1%
221551
 
< 0.1%
235591
 
< 0.1%
ValueCountFrequency (%)
4346335071
< 0.1%
4340582631
< 0.1%
4339883801
< 0.1%
4336591231
< 0.1%
4332585191
< 0.1%
4331221441
< 0.1%
4329173061
< 0.1%
4329032801
< 0.1%
4328432121
< 0.1%
4328425671
< 0.1%

host_name
Categorical

HIGH CARDINALITY

Distinct6878
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size293.3 KiB
Marie
 
390
Pierre
 
367
Antoine
 
269
Guillaume
 
266
Nicolas
 
259
Other values (6873)
35970 

Length

Max length30
Median length6
Mean length7.135923883
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4302 ?
Unique (%)11.5%

Sample

1st rowBorzou
2nd rowFranck
3rd rowAnaïs
4th rowElisabeth
5th rowMichael

Common Values

ValueCountFrequency (%)
Marie390
 
1.0%
Pierre367
 
1.0%
Antoine269
 
0.7%
Guillaume266
 
0.7%
Nicolas259
 
0.7%
Sophie257
 
0.7%
Checkmyguest242
 
0.6%
Camille233
 
0.6%
Thomas229
 
0.6%
Anne226
 
0.6%
Other values (6868)34783
92.7%

Length

2022-03-01T17:55:37.792670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
marie391
 
1.0%
pierre367
 
1.0%
checkmyguest344
 
0.9%
antoine269
 
0.7%
guillaume266
 
0.7%
nicolas259
 
0.7%
sophie257
 
0.7%
camille233
 
0.6%
thomas229
 
0.6%
anne226
 
0.6%
Other values (6842)34680
92.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

neighbourhood
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size293.3 KiB
Buttes-Montmartre
4053 
Popincourt
3648 
Vaugirard
2837 
Entrepôt
2824 
Batignolles-Monceau
2246 
Other values (15)
21913 

Length

Max length19
Median length10
Mean length10.50137256
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHôtel-de-Ville
2nd rowHôtel-de-Ville
3rd rowOpéra
4th rowPopincourt
5th rowButtes-Montmartre

Common Values

ValueCountFrequency (%)
Buttes-Montmartre4053
 
10.8%
Popincourt3648
 
9.7%
Vaugirard2837
 
7.6%
Entrepôt2824
 
7.5%
Batignolles-Monceau2246
 
6.0%
Ménilmontant2108
 
5.6%
Buttes-Chaumont2102
 
5.6%
Opéra1946
 
5.2%
Temple1832
 
4.9%
Reuilly1622
 
4.3%
Other values (10)12303
32.8%

Length

2022-03-01T17:55:38.036858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
buttes-montmartre4053
 
10.8%
popincourt3648
 
9.7%
vaugirard2837
 
7.6%
entrepôt2824
 
7.5%
batignolles-monceau2246
 
6.0%
ménilmontant2108
 
5.6%
buttes-chaumont2102
 
5.6%
opéra1946
 
5.2%
temple1832
 
4.9%
reuilly1622
 
4.3%
Other values (10)12303
32.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7985
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.86388311
Minimum48.81258
Maximum48.90486
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:38.259299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum48.81258
5-th percentile48.83222
Q148.85071
median48.86515
Q348.87839
95-th percentile48.89153
Maximum48.90486
Range0.09228
Interquartile range (IQR)0.02768

Descriptive statistics

Standard deviation0.01817231131
Coefficient of variation (CV)0.000371896586
Kurtosis-0.7594820155
Mean48.86388311
Median Absolute Deviation (MAD)0.01386
Skewness-0.2248378039
Sum1833421.758
Variance0.0003302328982
MonotonicityNot monotonic
2022-03-01T17:55:38.475033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.8566333
 
0.1%
48.8535829
 
0.1%
48.863927
 
0.1%
48.8588921
 
0.1%
48.8784521
 
0.1%
48.879321
 
0.1%
48.8658521
 
0.1%
48.8836920
 
0.1%
48.8686220
 
0.1%
48.8638919
 
0.1%
Other values (7975)37289
99.4%
ValueCountFrequency (%)
48.812581
< 0.1%
48.8161
< 0.1%
48.816151
< 0.1%
48.816181
< 0.1%
48.816311
< 0.1%
48.816421
< 0.1%
48.816551
< 0.1%
48.816741
< 0.1%
48.816771
< 0.1%
48.81691
< 0.1%
ValueCountFrequency (%)
48.904861
< 0.1%
48.904111
< 0.1%
48.903891
< 0.1%
48.902331
< 0.1%
48.90171
< 0.1%
48.901591
< 0.1%
48.901581
< 0.1%
48.901391
< 0.1%
48.901231
< 0.1%
48.90121
< 0.1%

longitude
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12729
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.34619686
Minimum2.22929
Maximum2.47203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:38.937647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.22929
5-th percentile2.28687
Q12.32614
median2.34901
Q32.37045
95-th percentile2.39615
Maximum2.47203
Range0.24274
Interquartile range (IQR)0.04431

Descriptive statistics

Standard deviation0.03247899372
Coefficient of variation (CV)0.01384325172
Kurtosis-0.335308882
Mean2.34619686
Median Absolute Deviation (MAD)0.02213
Skewness-0.3692791916
Sum88031.6524
Variance0.001054885033
MonotonicityNot monotonic
2022-03-01T17:55:39.323930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2852628
 
0.1%
2.336822
 
0.1%
2.3439918
 
< 0.1%
2.3367418
 
< 0.1%
2.3469918
 
< 0.1%
2.3788818
 
< 0.1%
2.35818
 
< 0.1%
2.3567218
 
< 0.1%
2.3465517
 
< 0.1%
2.284617
 
< 0.1%
Other values (12719)37329
99.5%
ValueCountFrequency (%)
2.229291
< 0.1%
2.235491
< 0.1%
2.238571
< 0.1%
2.242731
< 0.1%
2.2470268851
< 0.1%
2.248041
< 0.1%
2.25071
< 0.1%
2.251621
< 0.1%
2.251731
< 0.1%
2.2518151
< 0.1%
ValueCountFrequency (%)
2.472031
< 0.1%
2.467121
< 0.1%
2.461131
< 0.1%
2.459651
< 0.1%
2.456991
< 0.1%
2.456471
< 0.1%
2.454991
< 0.1%
2.450411
< 0.1%
2.449881
< 0.1%
2.449741
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.3 KiB
Entirehome/apt
31643 
Privateroom
4954 
Hotelroom
 
709
Sharedroom
 
215

Length

Max length14
Median length14
Mean length13.48650089
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntirehome/apt
2nd rowEntirehome/apt
3rd rowEntirehome/apt
4th rowEntirehome/apt
5th rowEntirehome/apt

Common Values

ValueCountFrequency (%)
Entirehome/apt31643
84.3%
Privateroom4954
 
13.2%
Hotelroom709
 
1.9%
Sharedroom215
 
0.6%

Length

2022-03-01T17:55:39.621255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-01T17:55:39.772034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
entirehome/apt31643
84.3%
privateroom4954
 
13.2%
hotelroom709
 
1.9%
sharedroom215
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct480
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.3246982
Minimum8
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:40.028417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile38
Q160
median90
Q3133
95-th percentile264
Maximum499
Range491
Interquartile range (IQR)73

Descriptive statistics

Standard deviation73.93341657
Coefficient of variation (CV)0.6701438372
Kurtosis4.834019033
Mean110.3246982
Median Absolute Deviation (MAD)31
Skewness1.98845833
Sum4139493
Variance5466.150085
MonotonicityNot monotonic
2022-03-01T17:55:40.344479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
601494
 
4.0%
801466
 
3.9%
701418
 
3.8%
501342
 
3.6%
1001302
 
3.5%
901279
 
3.4%
651039
 
2.8%
120945
 
2.5%
75925
 
2.5%
55809
 
2.2%
Other values (470)25502
68.0%
ValueCountFrequency (%)
82
 
< 0.1%
91
 
< 0.1%
109
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
142
 
< 0.1%
157
< 0.1%
164
 
< 0.1%
176
 
< 0.1%
1815
< 0.1%
ValueCountFrequency (%)
4992
 
< 0.1%
4984
 
< 0.1%
4973
 
< 0.1%
4952
 
< 0.1%
4942
 
< 0.1%
4932
 
< 0.1%
4911
 
< 0.1%
49015
< 0.1%
4893
 
< 0.1%
4883
 
< 0.1%

minimum_nights
Real number (ℝ≥0)

Distinct76
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.76954239
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:40.704704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q330
95-th percentile365
Maximum9999
Range9998
Interquartile range (IQR)28

Descriptive statistics

Standard deviation161.4826989
Coefficient of variation (CV)1.779040575
Kurtosis377.0202976
Mean90.76954239
Median Absolute Deviation (MAD)2
Skewness7.223455733
Sum3405764
Variance26076.66205
MonotonicityNot monotonic
2022-03-01T17:55:41.014260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3658801
23.5%
27458
19.9%
16725
17.9%
35260
14.0%
42633
 
7.0%
52070
 
5.5%
301648
 
4.4%
7900
 
2.4%
6654
 
1.7%
10222
 
0.6%
Other values (66)1150
 
3.1%
ValueCountFrequency (%)
16725
17.9%
27458
19.9%
35260
14.0%
42633
 
7.0%
52070
 
5.5%
6654
 
1.7%
7900
 
2.4%
899
 
0.3%
944
 
0.1%
10222
 
0.6%
ValueCountFrequency (%)
99991
 
< 0.1%
11241
 
< 0.1%
10004
 
< 0.1%
9991
 
< 0.1%
5231
 
< 0.1%
5002
 
< 0.1%
4002
 
< 0.1%
3658801
23.5%
3602
 
< 0.1%
3251
 
< 0.1%

number_of_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct439
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.15932411
Minimum1
Maximum1809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:41.266109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median11
Q330
95-th percentile115
Maximum1809
Range1808
Interquartile range (IQR)26

Descriptive statistics

Standard deviation51.28116702
Coefficient of variation (CV)1.821107879
Kurtosis74.80869972
Mean28.15932411
Median Absolute Deviation (MAD)9
Skewness5.716000292
Sum1056566
Variance2629.758091
MonotonicityNot monotonic
2022-03-01T17:55:41.556997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13969
 
10.6%
22964
 
7.9%
32370
 
6.3%
41954
 
5.2%
51740
 
4.6%
61395
 
3.7%
71310
 
3.5%
81157
 
3.1%
91023
 
2.7%
10862
 
2.3%
Other values (429)18777
50.0%
ValueCountFrequency (%)
13969
10.6%
22964
7.9%
32370
6.3%
41954
5.2%
51740
4.6%
61395
 
3.7%
71310
 
3.5%
81157
 
3.1%
91023
 
2.7%
10862
 
2.3%
ValueCountFrequency (%)
18091
< 0.1%
11131
< 0.1%
8971
< 0.1%
8351
< 0.1%
8171
< 0.1%
8121
< 0.1%
7691
< 0.1%
6921
< 0.1%
6901
< 0.1%
6731
< 0.1%

reviews_per_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct761
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8135380187
Minimum0.01
Maximum50.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:41.847477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.13
median0.38
Q31
95-th percentile3.02
Maximum50.86
Range50.85
Interquartile range (IQR)0.87

Descriptive statistics

Standard deviation1.204075876
Coefficient of variation (CV)1.480048687
Kurtosis115.2374979
Mean0.8135380187
Median Absolute Deviation (MAD)0.31
Skewness5.901227014
Sum30524.76
Variance1.449798716
MonotonicityNot monotonic
2022-03-01T17:55:42.159161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.041117
 
3.0%
0.031005
 
2.7%
0.07874
 
2.3%
0.06808
 
2.2%
0.05788
 
2.1%
0.02784
 
2.1%
0.08709
 
1.9%
0.09682
 
1.8%
0.12614
 
1.6%
0.1608
 
1.6%
Other values (751)29532
78.7%
ValueCountFrequency (%)
0.01594
1.6%
0.02784
2.1%
0.031005
2.7%
0.041117
3.0%
0.05788
2.1%
0.06808
2.2%
0.07874
2.3%
0.08709
1.9%
0.09682
1.8%
0.1608
1.6%
ValueCountFrequency (%)
50.861
< 0.1%
24.21
< 0.1%
22.741
< 0.1%
22.111
< 0.1%
21.791
< 0.1%
21.411
< 0.1%
21.321
< 0.1%
21.021
< 0.1%
19.91
< 0.1%
19.391
< 0.1%

calculated_host_listings_count
Real number (ℝ≥0)

Distinct64
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.279523467
Minimum1
Maximum252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:42.438224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile51
Maximum252
Range251
Interquartile range (IQR)1

Descriptive statistics

Standard deviation32.52443085
Coefficient of variation (CV)3.504967789
Kurtosis30.63719923
Mean9.279523467
Median Absolute Deviation (MAD)0
Skewness5.358767207
Sum348177
Variance1057.838602
MonotonicityNot monotonic
2022-03-01T17:55:42.731231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127585
73.5%
22911
 
7.8%
3897
 
2.4%
4545
 
1.5%
5466
 
1.2%
6361
 
1.0%
9239
 
0.6%
7235
 
0.6%
8223
 
0.6%
10211
 
0.6%
Other values (54)3848
 
10.3%
ValueCountFrequency (%)
127585
73.5%
22911
 
7.8%
3897
 
2.4%
4545
 
1.5%
5466
 
1.2%
6361
 
1.0%
7235
 
0.6%
8223
 
0.6%
9239
 
0.6%
10211
 
0.6%
ValueCountFrequency (%)
252205
0.5%
22089
0.2%
20648
 
0.1%
196143
0.4%
195157
0.4%
141114
0.3%
135133
0.4%
11213
 
< 0.1%
11111
 
< 0.1%
9994
0.3%

availability_365
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct366
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.54556115
Minimum0
Maximum365
Zeros17285
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:43.023994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q3199
95-th percentile355
Maximum365
Range365
Interquartile range (IQR)199

Descriptive statistics

Standard deviation132.3158366
Coefficient of variation (CV)1.329198761
Kurtosis-0.7565139049
Mean99.54556115
Median Absolute Deviation (MAD)6
Skewness0.9410688342
Sum3735049
Variance17507.48061
MonotonicityNot monotonic
2022-03-01T17:55:43.267033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017285
46.1%
1422
 
1.1%
365389
 
1.0%
364331
 
0.9%
2291
 
0.8%
3252
 
0.7%
4235
 
0.6%
8221
 
0.6%
363208
 
0.6%
342202
 
0.5%
Other values (356)17685
47.1%
ValueCountFrequency (%)
017285
46.1%
1422
 
1.1%
2291
 
0.8%
3252
 
0.7%
4235
 
0.6%
5193
 
0.5%
6165
 
0.4%
7162
 
0.4%
8221
 
0.6%
9120
 
0.3%
ValueCountFrequency (%)
365389
1.0%
364331
0.9%
363208
0.6%
362178
0.5%
36192
 
0.2%
36099
 
0.3%
359107
 
0.3%
358130
 
0.3%
357108
 
0.3%
356132
 
0.4%

number_of_reviews_ltm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct141
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.744036673
Minimum0
Maximum1705
Zeros19197
Zeros (%)51.2%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:43.512486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile22
Maximum1705
Range1705
Interquartile range (IQR)5

Descriptive statistics

Standard deviation14.76882425
Coefficient of variation (CV)3.113134503
Kurtosis4835.009047
Mean4.744036673
Median Absolute Deviation (MAD)0
Skewness47.0573508
Sum178001
Variance218.1181697
MonotonicityNot monotonic
2022-03-01T17:55:43.760010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019197
51.2%
13305
 
8.8%
22194
 
5.8%
31677
 
4.5%
41367
 
3.6%
51148
 
3.1%
6930
 
2.5%
7817
 
2.2%
8691
 
1.8%
9587
 
1.6%
Other values (131)5608
 
14.9%
ValueCountFrequency (%)
019197
51.2%
13305
 
8.8%
22194
 
5.8%
31677
 
4.5%
41367
 
3.6%
51148
 
3.1%
6930
 
2.5%
7817
 
2.2%
8691
 
1.8%
9587
 
1.6%
ValueCountFrequency (%)
17051
< 0.1%
6271
< 0.1%
4101
< 0.1%
3951
< 0.1%
3631
< 0.1%
3211
< 0.1%
3051
< 0.1%
2801
< 0.1%
2551
< 0.1%
2461
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.8 KiB
False
31138 
True
6383 
ValueCountFrequency (%)
False31138
83.0%
True6383
 
17.0%
2022-03-01T17:55:43.929953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

accommodates
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0152981
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:44.019963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile6
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.532512073
Coefficient of variation (CV)0.5082456269
Kurtosis4.888414148
Mean3.0152981
Median Absolute Deviation (MAD)1
Skewness1.652557513
Sum113137
Variance2.348593254
MonotonicityNot monotonic
2022-03-01T17:55:44.185661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
218615
49.6%
49059
24.1%
33483
 
9.3%
62129
 
5.7%
11932
 
5.1%
51401
 
3.7%
8447
 
1.2%
7254
 
0.7%
1098
 
0.3%
942
 
0.1%
Other values (6)61
 
0.2%
ValueCountFrequency (%)
11932
 
5.1%
218615
49.6%
33483
 
9.3%
49059
24.1%
51401
 
3.7%
62129
 
5.7%
7254
 
0.7%
8447
 
1.2%
942
 
0.1%
1098
 
0.3%
ValueCountFrequency (%)
1612
 
< 0.1%
153
 
< 0.1%
148
 
< 0.1%
132
 
< 0.1%
1228
 
0.1%
118
 
< 0.1%
1098
 
0.3%
942
 
0.1%
8447
1.2%
7254
0.7%

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.087044588
Minimum0
Maximum50
Zeros7108
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:44.388081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.041793399
Coefficient of variation (CV)0.9583722782
Kurtosis928.9569082
Mean1.087044588
Median Absolute Deviation (MAD)0
Skewness20.52309613
Sum40787
Variance1.085333485
MonotonicityNot monotonic
2022-03-01T17:55:44.557103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
122891
61.0%
07108
 
18.9%
25499
 
14.7%
31610
 
4.3%
4351
 
0.9%
546
 
0.1%
507
 
< 0.1%
65
 
< 0.1%
73
 
< 0.1%
331
 
< 0.1%
ValueCountFrequency (%)
07108
 
18.9%
122891
61.0%
25499
 
14.7%
31610
 
4.3%
4351
 
0.9%
546
 
0.1%
65
 
< 0.1%
73
 
< 0.1%
331
 
< 0.1%
507
 
< 0.1%
ValueCountFrequency (%)
507
 
< 0.1%
331
 
< 0.1%
73
 
< 0.1%
65
 
< 0.1%
546
 
0.1%
4351
 
0.9%
31610
 
4.3%
25499
 
14.7%
122891
61.0%
07108
 
18.9%

beds
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.682764319
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:44.729728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum90
Range89
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.37143552
Coefficient of variation (CV)0.8149896602
Kurtosis1663.565841
Mean1.682764319
Median Absolute Deviation (MAD)0
Skewness28.73226247
Sum63139
Variance1.880835386
MonotonicityNot monotonic
2022-03-01T17:55:44.902739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
121240
56.6%
210761
28.7%
33403
 
9.1%
41323
 
3.5%
5484
 
1.3%
6195
 
0.5%
758
 
0.2%
830
 
0.1%
915
 
< 0.1%
182
 
< 0.1%
Other values (8)10
 
< 0.1%
ValueCountFrequency (%)
121240
56.6%
210761
28.7%
33403
 
9.1%
41323
 
3.5%
5484
 
1.3%
6195
 
0.5%
758
 
0.2%
830
 
0.1%
915
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
901
 
< 0.1%
851
 
< 0.1%
831
 
< 0.1%
791
 
< 0.1%
771
 
< 0.1%
182
 
< 0.1%
122
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
915
< 0.1%

availability_60
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.34004957
Minimum0
Maximum60
Zeros19762
Zeros (%)52.7%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:45.105621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326
95-th percentile57
Maximum60
Range60
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.38648436
Coefficient of variation (CV)1.453254297
Kurtosis-0.0240859937
Mean13.34004957
Median Absolute Deviation (MAD)0
Skewness1.202893339
Sum500532
Variance375.8357758
MonotonicityNot monotonic
2022-03-01T17:55:45.315739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019762
52.7%
1877
 
2.3%
59693
 
1.8%
60646
 
1.7%
6596
 
1.6%
2563
 
1.5%
3510
 
1.4%
4480
 
1.3%
37457
 
1.2%
7439
 
1.2%
Other values (51)12498
33.3%
ValueCountFrequency (%)
019762
52.7%
1877
 
2.3%
2563
 
1.5%
3510
 
1.4%
4480
 
1.3%
5437
 
1.2%
6596
 
1.6%
7439
 
1.2%
8342
 
0.9%
9299
 
0.8%
ValueCountFrequency (%)
60646
1.7%
59693
1.8%
58434
1.2%
57335
0.9%
56146
 
0.4%
55173
 
0.5%
54160
 
0.4%
53276
 
0.7%
52195
 
0.5%
51211
 
0.6%

number_of_reviews_l30d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5976919592
Minimum0
Maximum62
Zeros28166
Zeros (%)75.1%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:45.541086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.568395892
Coefficient of variation (CV)2.624087322
Kurtosis267.9796004
Mean0.5976919592
Median Absolute Deviation (MAD)0
Skewness10.05920398
Sum22426
Variance2.459865675
MonotonicityNot monotonic
2022-03-01T17:55:45.764625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
028166
75.1%
13969
 
10.6%
22291
 
6.1%
31349
 
3.6%
4742
 
2.0%
5469
 
1.2%
6218
 
0.6%
7123
 
0.3%
870
 
0.2%
940
 
0.1%
Other values (18)84
 
0.2%
ValueCountFrequency (%)
028166
75.1%
13969
 
10.6%
22291
 
6.1%
31349
 
3.6%
4742
 
2.0%
5469
 
1.2%
6218
 
0.6%
7123
 
0.3%
870
 
0.2%
940
 
0.1%
ValueCountFrequency (%)
623
< 0.1%
472
< 0.1%
411
 
< 0.1%
331
 
< 0.1%
252
< 0.1%
243
< 0.1%
233
< 0.1%
223
< 0.1%
201
 
< 0.1%
192
< 0.1%

availability_90
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.85818608
Minimum0
Maximum90
Zeros18977
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:45.999168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q348
95-th percentile87
Maximum90
Range90
Interquartile range (IQR)48

Descriptive statistics

Standard deviation31.23421666
Coefficient of variation (CV)1.366434613
Kurtosis-0.6287322351
Mean22.85818608
Median Absolute Deviation (MAD)0
Skewness0.9869894776
Sum857662
Variance975.5762902
MonotonicityNot monotonic
2022-03-01T17:55:46.246004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018977
50.6%
1681
 
1.8%
89673
 
1.8%
90632
 
1.7%
2465
 
1.2%
88429
 
1.1%
67405
 
1.1%
8405
 
1.1%
3380
 
1.0%
4368
 
1.0%
Other values (81)14106
37.6%
ValueCountFrequency (%)
018977
50.6%
1681
 
1.8%
2465
 
1.2%
3380
 
1.0%
4368
 
1.0%
5297
 
0.8%
6263
 
0.7%
7289
 
0.8%
8405
 
1.1%
9252
 
0.7%
ValueCountFrequency (%)
90632
1.7%
89673
1.8%
88429
1.1%
87335
0.9%
86141
 
0.4%
85174
 
0.5%
84144
 
0.4%
83264
 
0.7%
82182
 
0.5%
81196
 
0.5%

review_scores_accuracy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct155
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.761403481
Minimum0
Maximum5
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:46.503361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14.7
median4.88
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.4199179117
Coefficient of variation (CV)0.08819204535
Kurtosis34.65292205
Mean4.761403481
Median Absolute Deviation (MAD)0.12
Skewness-4.882840564
Sum178652.62
Variance0.1763310526
MonotonicityNot monotonic
2022-03-01T17:55:46.729534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512648
33.7%
4.51125
 
3.0%
4.671073
 
2.9%
41051
 
2.8%
4.75976
 
2.6%
4.88934
 
2.5%
4.83853
 
2.3%
4.8849
 
2.3%
4.92791
 
2.1%
4.86787
 
2.1%
Other values (145)16434
43.8%
ValueCountFrequency (%)
015
 
< 0.1%
1127
0.3%
1.331
 
< 0.1%
1.55
 
< 0.1%
1.671
 
< 0.1%
284
0.2%
2.21
 
< 0.1%
2.251
 
< 0.1%
2.338
 
< 0.1%
2.381
 
< 0.1%
ValueCountFrequency (%)
512648
33.7%
4.9972
 
0.2%
4.98211
 
0.6%
4.97358
 
1.0%
4.96478
 
1.3%
4.95621
 
1.7%
4.94646
 
1.7%
4.93704
 
1.9%
4.92791
 
2.1%
4.91655
 
1.7%

review_scores_cleanliness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct210
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.595848991
Minimum0
Maximum5
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:46.955053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.67
Q14.46
median4.75
Q34.96
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5298715744
Coefficient of variation (CV)0.1152935128
Kurtosis14.54296392
Mean4.595848991
Median Absolute Deviation (MAD)0.25
Skewness-3.07854283
Sum172440.85
Variance0.2807638854
MonotonicityNot monotonic
2022-03-01T17:55:47.273265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58902
23.7%
41841
 
4.9%
4.51577
 
4.2%
4.671252
 
3.3%
4.75992
 
2.6%
4.8757
 
2.0%
4.33650
 
1.7%
4.86645
 
1.7%
4.83642
 
1.7%
4.88621
 
1.7%
Other values (200)19642
52.3%
ValueCountFrequency (%)
015
 
< 0.1%
1175
0.5%
1.511
 
< 0.1%
1.61
 
< 0.1%
1.674
 
< 0.1%
1.81
 
< 0.1%
2137
0.4%
2.22
 
< 0.1%
2.254
 
< 0.1%
2.291
 
< 0.1%
ValueCountFrequency (%)
58902
23.7%
4.9952
 
0.1%
4.98147
 
0.4%
4.97204
 
0.5%
4.96276
 
0.7%
4.95370
 
1.0%
4.94370
 
1.0%
4.93404
 
1.1%
4.92527
 
1.4%
4.91423
 
1.1%

review_scores_checkin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct164
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.803448735
Minimum0
Maximum5
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:47.471677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2
Q14.77
median4.93
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.3958160087
Coefficient of variation (CV)0.08240246342
Kurtosis43.41299031
Mean4.803448735
Median Absolute Deviation (MAD)0.07
Skewness-5.494390089
Sum180230.2
Variance0.1566703127
MonotonicityNot monotonic
2022-03-01T17:55:47.661513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
514952
39.8%
4.67872
 
2.3%
4.5841
 
2.2%
4.83840
 
2.2%
4.92824
 
2.2%
4.88823
 
2.2%
4.94791
 
2.1%
4.75791
 
2.1%
4.93786
 
2.1%
4786
 
2.1%
Other values (154)15215
40.6%
ValueCountFrequency (%)
017
 
< 0.1%
1122
0.3%
1.51
 
< 0.1%
256
0.1%
2.21
 
< 0.1%
2.251
 
< 0.1%
2.333
 
< 0.1%
2.513
 
< 0.1%
2.62
 
< 0.1%
2.631
 
< 0.1%
ValueCountFrequency (%)
514952
39.8%
4.99126
 
0.3%
4.98355
 
0.9%
4.97528
 
1.4%
4.96657
 
1.8%
4.95722
 
1.9%
4.94791
 
2.1%
4.93786
 
2.1%
4.92824
 
2.2%
4.91744
 
2.0%

review_scores_communication
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct159
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.814802644
Minimum0
Maximum5
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:47.861371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2
Q14.79
median4.95
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.3971398935
Coefficient of variation (CV)0.08248310945
Kurtosis43.46791092
Mean4.814802644
Median Absolute Deviation (MAD)0.05
Skewness-5.582298749
Sum180656.21
Variance0.157720095
MonotonicityNot monotonic
2022-03-01T17:55:48.042786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
516079
42.9%
4.92869
 
2.3%
4.88822
 
2.2%
4.94794
 
2.1%
4.95786
 
2.1%
4.5785
 
2.1%
4.67767
 
2.0%
4.93759
 
2.0%
4.83753
 
2.0%
4752
 
2.0%
Other values (149)14355
38.3%
ValueCountFrequency (%)
012
 
< 0.1%
1131
0.3%
1.53
 
< 0.1%
1.61
 
< 0.1%
1.671
 
< 0.1%
262
0.2%
2.252
 
< 0.1%
2.338
 
< 0.1%
2.43
 
< 0.1%
2.516
 
< 0.1%
ValueCountFrequency (%)
516079
42.9%
4.99182
 
0.5%
4.98474
 
1.3%
4.97622
 
1.7%
4.96671
 
1.8%
4.95786
 
2.1%
4.94794
 
2.1%
4.93759
 
2.0%
4.92869
 
2.3%
4.91658
 
1.8%

review_scores_location
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct153
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.805459609
Minimum0
Maximum5
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:48.243045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.24
Q14.75
median4.92
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.3476724963
Coefficient of variation (CV)0.07234947842
Kurtosis47.05824889
Mean4.805459609
Median Absolute Deviation (MAD)0.08
Skewness-5.330315763
Sum180305.65
Variance0.1208761647
MonotonicityNot monotonic
2022-03-01T17:55:48.434741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
514030
37.4%
4.51093
 
2.9%
4.671012
 
2.7%
4944
 
2.5%
4.75931
 
2.5%
4.88893
 
2.4%
4.8855
 
2.3%
4.92825
 
2.2%
4.83822
 
2.2%
4.86808
 
2.2%
Other values (143)15308
40.8%
ValueCountFrequency (%)
015
 
< 0.1%
167
0.2%
1.52
 
< 0.1%
228
0.1%
2.55
 
< 0.1%
2.671
 
< 0.1%
2.712
 
< 0.1%
2.752
 
< 0.1%
2.831
 
< 0.1%
2.91
 
< 0.1%
ValueCountFrequency (%)
514030
37.4%
4.99175
 
0.5%
4.98391
 
1.0%
4.97549
 
1.5%
4.96626
 
1.7%
4.95719
 
1.9%
4.94727
 
1.9%
4.93751
 
2.0%
4.92825
 
2.2%
4.91697
 
1.9%

review_scores_value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct176
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.619646331
Minimum0
Maximum5
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size293.3 KiB
2022-03-01T17:55:48.607526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14.5
median4.72
Q34.9
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.4616484383
Coefficient of variation (CV)0.09993155431
Kurtosis21.44012332
Mean4.619646331
Median Absolute Deviation (MAD)0.21
Skewness-3.646082196
Sum173333.75
Variance0.2131192806
MonotonicityNot monotonic
2022-03-01T17:55:48.779194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57838
20.9%
4.51888
 
5.0%
41753
 
4.7%
4.671533
 
4.1%
4.751282
 
3.4%
4.8956
 
2.5%
4.83790
 
2.1%
4.71707
 
1.9%
4.33684
 
1.8%
4.6680
 
1.8%
Other values (166)19410
51.7%
ValueCountFrequency (%)
013
 
< 0.1%
1144
0.4%
1.331
 
< 0.1%
1.56
 
< 0.1%
1.81
 
< 0.1%
296
0.3%
2.252
 
< 0.1%
2.338
 
< 0.1%
2.43
 
< 0.1%
2.533
 
0.1%
ValueCountFrequency (%)
57838
20.9%
4.993
 
< 0.1%
4.9812
 
< 0.1%
4.9737
 
0.1%
4.9675
 
0.2%
4.95126
 
0.3%
4.94163
 
0.4%
4.93211
 
0.6%
4.92325
 
0.9%
4.91272
 
0.7%

Interactions

2022-03-01T17:55:24.379098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:35.943337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:40.631441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:46.199587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:50.607109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:55.402380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:01.018902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:06.041863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:11.286642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:16.346128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:21.128267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:27.216098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:32.460609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:37.726660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:44.012427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:48.749976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:53.058230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:57.198082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:00.910084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:05.916855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:11.006526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:15.426367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:19.911151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:24.547974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:36.163577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:40.816924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:46.348956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:50.832766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:55.741279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:01.239471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:06.221547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:11.452143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:16.570327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:21.369353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:27.410762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:32.621366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:37.956829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:44.233619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:48.895654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:53.237576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:57.335260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:01.087014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:06.099458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-01T17:54:05.310638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:10.520288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:15.419328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:20.220595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:26.458172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:31.679608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:36.645517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:42.835363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:48.112502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:52.347038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:56.521811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:00.261910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:04.940652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:10.217407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:14.667729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:19.149663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:23.672852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:28.121214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:39.791704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:45.710362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:50.065271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:54.870259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:00.358855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:05.496576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:10.701949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:15.747870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:20.498759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:26.620580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:31.840962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:36.880653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:43.296642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:48.261515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:52.549240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:56.677709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:00.435663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:05.093601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:10.386711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:14.838352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:19.313916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:23.835559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:29.037291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:40.089648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:45.863776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:50.261777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:55.031705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:00.548529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:05.693231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:10.879301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:15.993123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:20.702303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:26.813095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:32.031746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:37.050905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:43.555218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:48.433395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:52.712983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:56.829590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:00.614579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:05.264885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:10.571075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:14.998089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:19.523457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:23.997945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:29.255879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:40.395350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:46.019697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:50.454610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:53:55.205215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:00.865963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:05.881367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:11.059633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:16.182357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:20.892974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:27.048857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:32.251394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:37.459841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:43.737856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:48.609394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:52.894138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:54:57.021012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:00.763576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:05.662273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:10.729269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:15.174828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:19.707280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-01T17:55:24.181899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-01T17:55:48.983548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-01T17:55:49.414246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-01T17:55:49.777499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-01T17:55:50.088642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-01T17:55:50.254111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-01T17:55:29.618770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-01T17:55:32.895841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmhost_is_superhostaccommodatesbedroomsbedsavailability_60number_of_reviews_l30davailability_90review_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_value
05396Explore the heart of old Paris7903BorzouHôtel-de-Ville48.8524702.358350Entirehome/apt10222731.8015842f20.01.0283584.564.484.774.814.964.53
17397MARAIS - 2ROOMS APT - 2/4 PEOPLE2626FranckHôtel-de-Ville48.8590902.353150Entirehome/apt112102882.22220919t42.02.052244.794.434.914.884.924.71
27964Large & sunny flat with balcony !22155AnaïsOpéra48.8741702.342450Entirehome/apt130660.0413440f21.01.0390695.005.005.005.005.005.00
39952Paris petit coin douillet33534ElisabethPopincourt48.8637302.370930Entirehome/apt814330.3112607t21.01.030254.974.885.004.914.914.94
410586Studio 7 Montmartre37107MichaelButtes-Montmartre48.8870002.345310Entirehome/apt8030490.3441621t20.02.0290294.774.774.884.984.604.67
510588Studio 10 Montmartre37107MichaelButtes-Montmartre48.8872502.345180Entirehome/apt7530190.1542173t20.01.0100104.884.945.004.944.594.69
610917ELYSEES-PONCELET FLAT NEAR CH. ELYS39402IsabelleBatignolles-Monceau48.8790742.296904Entirehome/apt14330250.17100t41.02.00004.504.194.313.934.404.06
711213DOWNTOWN PARIS41322MathieuEntrepôt48.8710902.373760Privateroom17011511.9322524f61.03.00004.774.364.864.924.694.55
811265Elegant appartment in Montmartre41718SylvieButtes-Montmartre48.8849402.339970Entirehome/apt1007160.24100f21.01.00004.814.944.534.754.934.80
911487Heart of Paris, brand new aparment.42666BrigittePopincourt48.8644102.371390Entirehome/apt603040.0312162t20.01.0220224.254.754.754.754.754.25

Last rows

idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmhost_is_superhostaccommodatesbedroomsbedsavailability_60number_of_reviews_l30davailability_90review_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_value
3751153590336Appartement Cosy proche Montmartre66695202AnnalisaButtes-Montmartre48.8936512.346627Entirehome/apt90311.013611f21.01.0561865.05.05.05.05.05.0
3751253590461Appartement Cosy en plein coeur de Paris433988380LoubnaReuilly48.8385772.390153Entirehome/apt48311.013241f20.01.0191495.04.05.05.05.05.0
3751353598941SUPERB studio with BALCONY in the HEART OF PARIS434058263HenriMénilmontant48.8687592.399991Entirehome/apt62111.0131f20.01.03135.05.05.05.05.05.0
3751453600206Superbe appartement dans le 5ème160134472RitaPanthéon48.8428272.350496Entirehome/apt250111.03481f42.02.0101105.05.04.05.05.04.0
3751553616668Studio design avec vue sur la Tour Eiffel21238185EmilieVaugirard48.8436972.284839Entirehome/apt100111.0171t21.01.07175.05.05.05.05.05.0
3751653617391wide studio with a balcony and a convertible sofa5345079PruneGobelins48.8279602.368460Entirehome/apt39111.011331f21.01.02125.05.05.05.05.05.0
3751753638244Maison Boissiere | Deluxe apartment Arc de Triomphe51567288SweetInnPassy48.8681142.289649Entirehome/apt312211.0522581f41.02.0521825.05.05.05.05.05.0
3751853640687Female only! Femme seulement.46032712CheerPalais-Bourbon48.8580252.328207Sharedroom60111.011751f11.01.0551855.05.05.05.05.05.0
3751953657965Artist Atelier with garden - Tour Eiffel430505007ValentinVaugirard48.8493302.288450Entirehome/apt200111.033531f61.03.0481785.05.05.05.05.05.0
3752053669791Lovely studio walking distance from Champs-Élysées434633507ChristyÉlysée48.8737072.308950Entirehome/apt100222.01202f20.01.0202205.05.05.05.05.05.0